Mentoring partnerships for success : the role of mentoring in reconstructing professional identities and in creating a sense of belonging for internationally-educated teachers from visible minority groups in greater Toronto area school communities
Bibliographic record
Abstract
This study explores and analyses mentoring relationships between unemployed and underemployed internationally-educated teachers (IETs) from visible minority groups and Canadian-experienced educators, and their influence on the re-establishment of migrant teachers' professional identities and perceptions of inclusion in Greater Toronto Area (GTA) school communities. A detailed literature review summarizes previously identified issues in this area while, nine in-depth interviews conducted with mentees, mentors and mentoring pairs in this study identify prior and newly emergent themes. Primary themes that transpired include: the presence of varying forms of resistance from the dominant community towards IETs; the role of mentoring relationships in meeting IETs' needs; and the importance of consistency, trust and honesty in building collaborative relationships that foster IETs' successful integration into the teaching field. Recommendations include: the delivery of equity-oriented programming through educational bodies; the development of sustainable occupation-specific teacher mentoring programs; and the promotion of IETs to the greater community by educational stakeholders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".